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且构网 - 分享程序员编程开发的那些事

R中的多元时间序列建模

更新时间:2023-02-26 20:12:17

如果您还没有这样做,请查看 CRAN 上的时间序列视图,尤其是关于多元时间序列的部分.

If you haven't done so already, have a look at the time series view on CRAN, especially the section on multivariate time series.

在金融领域,一种传统的方法是使用因子模型,通常使用 BARRA 或 Fama-French 类型模型.Eric Zivot 的 使用 S-PLUS 建模金融时间序列" 很好地概述了这些主题,但它不能立即转移到 R. Ruey Tsay 的分析of Financial Time Series"(可在 CRAN 上的 TSA 包中找到)在第 9 章中也对因子模型和主成分分析进行了很好的讨论.

In finance, one traditional way of doing this is with a factor model, frequently with either a BARRA or Fama-French type model. Eric Zivot's "Modeling financial time series with S-PLUS" gives a good overview of these topics, but it isn't immediately transferable into R. Ruey Tsay's "Analysis of Financial Time Series" (available in the TSA package on CRAN) also has a nice discussion of factor models and principal component analysis in chapter 9.

R 还提供了许多涵盖 矢量自回归 (VAR) 模型的软件包.特别是,我建议查看 Bernhard Pfaff 的 VAR Modeling (vars) 包和相关小插图.

R also has a number of packages that cover vector autoregression (VAR) models. In particular, I would recommend looking at Bernhard Pfaff's VAR Modelling (vars) package and the related vignette.

我强烈建议您查看 Ruey Tsay 的主页因为它涵盖了所有这些主题,并提供了必要的 R 代码.特别是,请查看应用多元分析""金融时间序列分析"多元时间序列分析" 课程.

I strongly recommend looking at Ruey Tsay's homepage because it covers all these topics, and provides the necessary R code. In particular, look at the "Applied Multivariate Analysis", "Analysis of Financial Time Series", and "Multivariate Time Series Analysis" courses.

这是一个非常大的主题,有很多好书涵盖了它,包括多元时间序列预测和季节性.还有一些:

This is a very large subject and there are many good books that cover it, including both multivariate time series forcasting and seasonality. Here are a few more:

  1. Kleiber 和 Zeileis."应用计量经济学与 R" 没有解决这个问题具体来说,但它很好地涵盖了整个主题(另请参阅 CRAN 上的 AER 包).
  2. 舒威和斯托弗."时间序列分析及其应用:R 示例" 有多元 ARIMA 模型的例子.
  3. 哭泣者."时间序列分析:在 R 中的应用"是该主题的经典之作,已更新以包含 R 代码.
  1. Kleiber and Zeileis. "Applied Econometrics with R" doesn't address this specifically, but it covers the overall subject very well (see also the AER package on CRAN).
  2. Shumway and Stoffer. "Time Series Analysis and Its Applications: With R Examples" has examples of multivariate ARIMA models.
  3. Cryer. "Time Series Analysis: With Applications in R" is a classic on the subject, updated to include R code.